Technical Field
Embodiments of the present disclosure relate to the estimation of depth maps.
Description of the Related Art
In computer vision a depth map is usually defined as an array of data, which contains information identifying the distance of the surfaces of scene objects from a viewpoint. In particular, during the depth map estimation process, a depth value is associated with each pixel of the depth map. Accordingly, such a depth map may be considered as an image by simply considering the depth values of the depth map as grayscale values of an image.
Various methods exist in the prior art to obtain a depth map, such as e.g.:
stereo imaging, wherein the depth map is estimated from at least two images of the same object obtained from different angels of view; and
structured light patterns.
Specifically, the present disclosure relates to the second approach, wherein the scene is illuminated with a structured light pattern.
For example, as show in FIG. 1, a depth map estimation system operating according to this approach usually comprises a projector 10, a camera 20, and a processing unit 30.
In the example considered, the processing unit 30 sends a given pattern PAT to the projector 10, and the projector illuminates the scene with this pattern.
The camera 20 captures the image IMG of the scene, and thus the pattern PAT projected on the scene, and transmits the image IMG to the processing unit 30.
Finally, by comparing the image IMG with the initial pattern PAT, the processing unit may estimate the depth map DM from the displacement of the patterns in the image IMG.
Generally, the estimation process used to solve this type of problem can be grouped in two families:
multiple shot: in this case the depth information is extracted using several patterns and images; this method can produce excellent quality maps, but is usually only suitable for static scenes;
single shot: where the depth information is extracted using only one single pattern and image; these methods are usually used when it is not possible to acquire more than one image of the same scene, e.g., in presence of moving objects.
For an overview of possible prior-art solutions, reference can be made, e.g., to Maurice X., et al., “Epipolar Based Structured Light Pattern Design for 3-D Reconstruction of Moving Surfaces”, IEEE International Conference on Robotics and Automation, 2011, Shanghai, China; Albitar C., et al., “Design of Monochromatic Pattern for a Robust Structured Light Coding”, IEEE International
Conference on Image Processing, 2007; or Salvi J, et al., “A State of the Art in Structured Light Patterns for surface profilometry”, Journal in Pattern Recognition, Volume 43 Issue 8, 2010.